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Compounding Automation Features From Multiple APIs for Automated Linear Accelerator Quality Assurance

M Schmidt1,2*, C Raman1, Y Wu1, N Knutson1, F Reynoso1, Y Hao1, M Yaqoub1, G Hugo1, E Sajo2, B Sun1, (1) Washington University School of Medicine in St. Louis, Saint Louis, MO, AF,(2) Univ Massachusetts Lowell, Lowell, MA


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Quality assurance (QA) offers an opportunity for automation to have a meaningful impact in clinical efficiency without concerns of impacting patient-specific plan parameters. This work describes the process of generating and analyzing QA plans with the assistance of automation through multiple product APIs.

Methods: An automated quality assurance treatment plan generator and analysis suite was developed using a combination of the Eclipse Scripting API, ARIA Access Web Service and Portal Dosimetry API available within the Eclipse Treatment Planning System and ARIA Oncology Information System (Varian, Palo Alto, CA). Within the application, the user has the ability to create a new patient for which the QA plans will be associated, generate courses and plans of both static and dynamic treatment beams, and copy structure sets from other patient image sets or construct new structures with the API. These auto-generated plans were implemented clinically for routine daily, monthly, annual and electronic portal imaging device (EPID) quality assurance.

Results: Automated generation of QA plans led to more frequent QA plan generation and more consistent QA field parameters. Historical QA plans for daily QA output and constancy showed a range of field sizes from 20.0cm to 21.6cm in the X (lower) jaw size and 20.0cm to 20.4cm in the Y (upper) jaw size when compared over seven linear accelerators within our institution. Automated QA plan generation reduced field size deviations to a range of 0.16cm and 0.0cm for X and Y jaw, respectively.

Conclusion: Automated generation of linear accelerator quality assurance plans allows for the precise implementation of consistent plan deliveries across all machines within the clinical institution, thereby reducing the burden on the record and verify system to load high volume image data sets for daily imaging QA.

Funding Support, Disclosures, and Conflict of Interest: MCS Reports Consulting, Varian. GH reports honoraria / travel costs, Varian; research grants: Varian Medical Systems, Viewray, Siemens.


Quality Assurance, Portal Imaging, Linear Accelerator


TH- External Beam- Photons: Treatment planning using machine learning/Knowledge Based Planning/automation

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